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INVESTIGATING THE ROLE OF MONEY IN THE IDENTIFICATION OF MONETARY POLICY BEHAVIOR: A BAYESIAN DSGE PERSPECTIVE

Published online by Cambridge University Press:  24 January 2020

Bing Li ,
Qing Liu  and
Pei Pei
Bing Li*
Affiliation:
Tsinghua University
Qing Liu
Affiliation:
Tsinghua University
Pei Pei
Affiliation:
Central University of Finance and Economics
*
Address correspondence to: Bing Li, Academic Center for Chinese Economic Practice and Thinking, Tsinghua University, Room 128, ShunDe Building, Beijing 100084, China. e-mail: libing_econ@126.com.
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Abstract

This paper estimates an enriched version of the mainstream medium-scale dynamic stochastic general equilibrium model, which features nonseparability between consumption and real money balances in utility and a systematic response of the policy rate to money growth. Estimation results show that money is a significant factor in the monetary policy rule. As a consequence, econometric analysis that omits money from Taylor rules may lead to biased estimates of the model parameters. In contrast to earlier studies that rely on small-scale models, the paper stresses the merits of using a sufficiently rich model. First, it delivers different results, such as the role of nonseparability between consumption and money in utility. Second, the rich dynamics embedded in the model allow us to explore the responses of a larger set of macroeconomic variables, making the model more informative on the effects of shocks and more useful for understanding the sources of business cycles. Third and most importantly, it reveals the possible pitfalls of relying on small-scale models when studying money’s role in business cycles.

Keywords

MoneyMonetary Policy BehaviorDSGE ModelBayesian Methods
Type
Articles
Information
Macroeconomic Dynamics , Volume 25 , Issue 6 , September 2021 , pp. 1495 - 1537
Copyright
© Cambridge University Press 2020

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Footnotes

We would like to thank the editor, William Barnett, the associate editor, and the two anonymous referees for their helpful comments, which led to significant improvements in the paper. Meanwhile, we wish to thank Chong-En Bai, Alexander Kriwoluzky, Eric Leeper, Johannes Pfeifer, Chris Sims, Ben Zhe Wang, and Tao Zha for their many useful comments and suggestions. We also thank Qingquan Fan, Jinfeng Luo, and Haotian Jia for excellent research assistance. Bing Li acknowledges the support of the National Natural Science Foundation of China (No. 71103103) and Tsinghua University Initiative Scientific Research Program (No. 20151080392). Qing Liu would like to thank the support of the National Natural Science Foundation of China (No. 71773060) and Tsinghua University Initiative Scientific Research Program (No. 2016THZWJC11).

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